Chatgpt and passive cheating. A quasi-experimental study on ai assisted exam preparation
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
UMT.Lahore
Abstract
This study investigates the intersection of faculty stress, artificial intelligence (AI)
adoption in assessment design, and student academic performance through the lens of the Effort Reward Imbalance (ERI) model. Drawing on a quasi-experimental, quantitative research design,
data were collected from 130 higher education faculty members and four undergraduate classes
across diverse disciplines. The first hypothesis tested whether higher ERI among faculty members
predicted increased reliance on AI tools such as ChatGPT for designing assessments. Statistical
analysis, including t-tests and regression, revealed a significant positive relationship, indicating
that institutional stressors compel faculty to use AI not out of innovation, but as a coping
mechanism. The second hypothesis examined whether students who used AI for exam preparation
outperformed peers using traditional methods. Across all four subject classes, AI-prepared students
demonstrated significaThis study investigates the intersection of faculty stress, artificial intelligence (AI)
adoption in assessment design, and student academic performance through the lens of the Effort Reward Imbalance (ERI) model. Drawing on a quasi-experimental, quantitative research design,
data were collected from 130 higher education faculty members and four undergraduate classes
across diverse disciplines. The first hypothesis tested whether higher ERI among faculty members
predicted increased reliance on AI tools such as ChatGPT for designing assessments. Statistical
analysis, including t-tests and regression, revealed a significant positive relationship, indicating
that institutional stressors compel faculty to use AI not out of innovation, but as a coping
mechanism. The second hypothesis examined whether students who used AI for exam preparation
outperformed peers using traditional methods. Across all four subject classes, AI-prepared students
demonstrated significantly higher scores and moderate to large effect sizes, raising concerns about
“passive cheating”—a phenomenon where students gain unintended advantages due to congruence
between AI-assisted learning and AI-influenced assessment design. Subgroup analysis further
revealed that female faculty, mid-career academics, and those on research tracks reported higher
ERI levels. These findings underscore the cascading effects of institutional stress on both teaching
practices and student performance, highlighting the urgent need for policy reforms to address
workload equity, assessment integrity, and the ethical integration of AI in higher education.
Keywords: Effort-Reward Imbalance, AI in Education, Passive Cheating, ChatGPT-Assisted
Learning, Assessment Integritntly higher scores and moderate to large effect sizes, raising concerns about
“passive cheating”—a phenomenon where students gain unintended advantages due to congruence
between AI-assisted learning and AI-influenced assessment design. Subgroup analysis further
revealed that female faculty, mid-career academics, and those on research tracks reported higher
ERI levels. These findings underscore the cascading effects of institutional stress on both teaching
practices and student performance, highlighting the urgent need for policy reforms to address
workload equity, assessment integrity, and the ethical integration of AI in higher education.
Keywords: Effort-Reward Imbalance, AI in Education, Passive Cheating, ChatGPT-Assisted
Learning, Assessment Integrit